Nonlinear State Estimation Using Fuzzy Kalman Filter
| Autor: | R. Senthil, K. Janarthanan, J. Prakash |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Moving horizon estimation
State-space representation Computer science General Chemical Engineering Estimator General Chemistry Kalman filter Industrial and Manufacturing Engineering Invariant extended Kalman filter Extended Kalman filter Nonlinear system Control theory State space Fast Kalman filter Ensemble Kalman filter State observer Unscented transform Alpha beta filter |
| Zdroj: | Industrial & Engineering Chemistry Research. 45:8678-8688 |
| ISSN: | 1520-5045 0888-5885 |
| Popis: | In this paper, the authors have presented an approach for designing a nonlinear observer to estimate the states of a noisy dynamic system. The nonlinear observer design procedure involves representation of the nonlinear system as a family of local linear state space models; the state estimator for each linear local state space model uses standard Kalman filter theory and then a global state estimator is developed that combines the local state estimators. The effectiveness of the proposed fuzzy Kalman filter (nonlinear observer) has been demonstrated on a continuously stirred tank reactor (CSTR) process. The performances of the fuzzy Kalman filter (FKF) and the extended Kalman filter (EKF) have been compared in the presence of initial model/plant mismatch and input and output disturbances. Simulation studies also include an estimation of reactor concentration (inferential measurement), based only on the measured variable temperature of the reactor. |
| Databáze: | OpenAIRE |
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